Learning from binary labels with instance-dependent noise
Supervised learning has seen numerous theoretical and practical advances over the last few decades. However, its basic assumption of identical train and test distributions often fails to hold in practice. One important example of this is when the training instances are subject to label noise: that is, where the observed labels do not accurately reflect the underlying ground truth. While the impact of simple noise models has been extensively studied, relatively less attention has been paid to...[Show more]
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